Low frequency DAS well interference evaluation

ABSTRACT

A method of assessing cross-well interference and/or optimizing hydrocarbon production from a reservoir by obtaining low frequency DAS and DTS data and pressure data from a monitor well, when both the monitor and production well are shut-in, and then variably opening the production well for production, and detecting the temperature and pressure fluctuations that indication cross-well interference, and localizing the interference along the well length based on the low frequency DAS data. This information can be used to optimize well placement, completion plans, fracturing plans, and ultimately optimize production from a given reservoir.

PRIOR RELATED APPLICATIONS

This application claims priority to U.S. Ser. No. 62/649,346, filed Mar.28, 2018, and incorporated by reference in its entirety for allpurposes.

FIELD OF THE DISCLOSURE

The disclosure relates generally to methods of assessing cross wellinterference using Distributed Temperature Sensing (DTS), DistributedAcoustic Sensing (DAS), and pressure data.

BACKGROUND OF THE DISCLOSURE

Unconventional reservoirs include reservoirs such as tight-gas sands,gas and oil shales, coalbed methane, heavy oil and tar sands, andgas-hydrate deposits. These reservoirs have little to no porosity, thusthe hydrocarbons may be trapped within fractures and pore spaces of theformation. Additionally, the hydrocarbons may be adsorbed onto organicmaterial of a e.g. shale formation. Therefore, such reservoirs requirespecial recovery operations beyond the conventional operating practicesin order to mobilize and produce the oil.

The rapid development of extracting hydrocarbons from theseunconventional reservoirs can be tied to the combination of horizontaldrilling and induced fracturing (called “hydraulic fracturing” or simply“fracking”) of the formations. Horizontal drilling has allowed fordrilling along and within hydrocarbon reservoirs of a formation tobetter capture the hydrocarbons trapped within the reservoirs.Additionally, increasing the number of fractures in the formation and/orincreasing the size of existing fractures through fracking furtherincreases hydrocarbon recovery.

In a typical hydraulic fracturing treatment, fracturing treatment fluidis pumped downhole into the formation at a pressure sufficiently highenough to cause new fractures or to enlarge existing fractures in thereservoir. Next, frack fluid plus a proppant, such as sand, is pumpeddownhole. The proppant material remains in the fracture after thetreatment is completed, where it serves to hold the fracture open,thereby enhancing the ability of fluids to migrate from the formation tothe well bore through the fracture. The spacing between fractures aswell as the ability to stimulate fractures naturally present in the rockmay be major factors in the success of horizontal completions inunconventional hydrocarbon reservoirs.

While there are a great many fracking techniques, one useful techniqueis “plug-and-perf” fracking. Plug-and-perf completions are extremelyflexible multistage well completion techniques for cased hole wells.Each stage can be perforated and treated optimally because the fractureplan options can be modified in each stage. The engineer can applyknowledge from each previous stage to optimize treatment of the currentfrack stage.

The process consists of pumping a plug-and-perforating gun to a givendepth. The plug is set, the zone perforated, and the tools removed fromthe well. A ball is pumped downhole to isolate the zones below the plugand the fracture stimulation treatment is then pumped in, althoughwashing, etching, and other treatments may occur first depending ondownhole conditions. The ball-activated plug diverts fracture fluidsthrough the perforations into the formation, where pressure build-up andeventually causes fracturing. The fractures are held open by thesubsequent delivery of frack fluid plus a proppant—the small grains ofsand “propping” the fractures open. After the fracture stage iscompleted, the next plug and set of perforations are initiated, and theprocess is repeated moving further up the well.

One undesirable result of a fracking program occurs when the fracturesof one well reach to the fractures of a nearby well, causinginterference. Well interference has become an ever-increasing problem asthe spacing between wells has significantly decreased in recent years.Understanding the characteristics of well interference providesimportant insights for well spacing and completion design decisions.

Many methods have been developed to detect and analyze wellinterference, which includes: pressure monitoring, chemical/radioactivetracers, microseismic monitoring during stimulation, etc. However, allthese methods have limitations. For example, pressure analysis examinesthe pressure communication between the wells, but provides no spatialinformation about the connectivity. Tracers can provide spatialinformation, but both chemical and radioactive tracers can only measurewell interference during early production stages because of limiteddownhole survival time. Microseismic surveys can only providequalitative interpretations of the reservoir and the fractures, sincethere is no physical model that directly correlates microseismicity withfracture connectivity.

Distributed Acoustic Sensing (DAS) is an emerging fiber optic basedtechnology that has become available for the oil industry only in recentyears. The method requires an optical fiber attached to the wellbore toguide the laser energy into the reservoir. Each piece of the fibernaturally scatters a small portion of the laser energy back to thesurface sensing unit. The sensing unit then uses interferometrytechniques to measure the strain change along the fiber. The DAS dataare usually sampled at a very high rate (5000-10000 Hz) with a spatialresolution between 1-10 m. This high position accuracy provides criticalspatial data for detecting near well bore changes both in the wellundergoing stimulation and in an offset monitor well.

The raw DAS data are usually delivered in the form of optical phase,which ranges from −π to +π. The optical phase is defined by theinterference pattern of the laser energy back-scattered at two fiberlocations separated by a certain length. The separation length isusually referred as gauge length. The phase varies linearly with smalllength change between two locations, which can be approximated as theaxial strain change of the fiber between the locations. Depending on thesensing unit provider, the data deliverable is sometimes a timedifferential of the measured optical phase. In this case, the DAS datacan be treated as a linear-scaled strain rate.

DAS data have been used to monitor hydraulic fracturing operations inmany studies. The applications include injection fluid allocation,hydraulic fracture detection, microseismic monitoring, and others.However, most of these applications focus on the high frequency bands(>1 Hz) of the DAS data, and some applications only use the “intensity”of the signal, which is obtained through amplitude averaging processing.In this study, we demonstrate that DAS data in the low-frequency band(<1 Hz, preferably <0.1 Hz, or even <0.05 Hz) contain information thatcan provide critical information on cross well fluid communication.

Jin & Roy (2017) presented a novel method of using the low-frequency DASsignal to map fracture connections between nearby wells duringcompletion. However, mapping fracture connections during completioncannot provide information of the connectivity during production ofhydrocarbons. Thus, this early work by Jin & Roy needs to be furtherdeveloped to allow evaluation of well interference during oilproduction.

Thus, what is still needed in the art is a method of evaluating crosswell interference that includes spatial information along the wellbore.Even incremental improvements in technology can mean the differencebetween cost effective production and reserves that are uneconomical toproduce.

SUMMARY OF THE DISCLOSURE

Herein we present a new method of assessing cross-well interference thatuses fiber optical sensing technology to spatially locate and evaluatewell interference. This method requires optical fibers to be installedalong the wellbore, either through a permanent behind-casinginstallation, or through well intervention methods like wireline or coiltubing. Sensing units at the surface send laser pulses into the fiberand measure the reflected energy at each section of the fiber due to theglass impurities.

There are two fiber optical sensing techniques that are used in thismethod: Distributed Temperature Sensing (DTS) and Distributed AcousticSensing (DAS). DTS uses Raman scattering to measure absolute temperaturealong an optical fiber with around 1-ft spatial resolution and less than1 F accuracy. The sample rate of a DTS system ranges from 1 s to severalminutes.

DAS, by contrast, uses Rayleigh scattering to measure strain rate alongthe fiber. It usually has a spatial resolution around 1-10 meters, witha sample rate around 5-10 kHz. The measurement is sensitive to signalsin a very broad frequency range. At a very low-frequency band (<1 Hz,preferably <0.1 HZ, most preferred >0 and <0.05 HZ), it is sensitive tothe strain changes due to very small temperature perturbations. Thus,DAS can be used to measure temperature variation as small as 10⁻⁵ F/s.However, DAS cannot measure absolute temperature, making the temperaturechange relative in the downhole context. The low frequency band is alsoused because the resulting data contains polarity information.

By using the DAS low-frequency response, we can detect the smalltemperature perturbations induced by cross flows between the monitorwell perforations due to well interference during the production of theoperation well (FIG. 1). The connectivity between the wells can bequantitatively evaluated utilizing the DTS temperature gradient and thelow-frequency DAS signal as recorded on the monitor well.

However, collecting low frequency DAS can create challenges in dataanalysis. For example, there is almost always spike noise presentresulting from phase errors in the interrogator. In some applications,there is an extremely low-frequency (<1 mHz) drift signal that affectsall channels of the DAS interrogator and can be of greater strength(e.g. intensity) than the signal of interest.

Installation issues can also lead to interference. If the fiber opticcable used in DAS sensing is not directly coupled to the borehole, as isthe case for in temporary installations, there can be noise associatedwith vibrations in the fiber casing. The vibration noise can be ordersof magnitude higher than the signal of interest, thus effectivelymasking the signals. In thermal sensing applications, significantthermal dissipation, depending on material properties between the fluidand the fiber itself, can be exhibited.

Depending on the DAS application, installation and material effects, allor some of these interferences can affect the data sensing and/oracquisition. This leads to inaccurate results, time-consuming delays inoperation to gather additional data and/or extended analysis time by theoperator, and costly mistakes.

Thus, a new DAS processing workflow was designed to accurately “denoise”low-frequency DAS data for analysis in varied environments andapplications. The workflow determines which interferences or noisesource is present, and applies one or more correction techniques tomitigate or remove the interferences and/or noise from the acquireddata. This allows for selective modulation based on the characteristicspresent instead of a universal application of all techniques. In turn,the selective modulation reduces the time needed for correction of theacquisition data and speeds analysis. This methodology is described inUS20170260854 Low-frequency DAS SNR improvement (expressly incorporatedby reference in its entirety for all purposes), and can be employedherein.

The method generally proceeds as follows:

1. Select two hydraulically fractured wells suspected of wellinterference, and configure one as the monitor well and the other as theoperation or production well.

2. Install fiber optic cable in the monitor well, unless alreadypresent. The installation can either be permanent, behind the casing, orthrough well intervention methods using e.g., wireline, coil tubing, orcarbon rod.

3. Shut-in both wells for an extended period of time, to allow thetemperature and pressures to equilibrate (about 6-72 hrs, preferablyabout 12-24 hrs).

4. Begin simultaneously recording DAS and DTS for about 1-5 hours,preferably about 2-3 hours.

5. Open the operation or production well for hydrocarbon recovery andcontinuously record DAS and DTS data throughout this step. Some chokesize variation during the opening is recommended, as this will create apressure signal pattern that is easier to recognize in the DAS data.Alternatively, choke size can be changed to open and close the well at alater time period.

6. Analyze the data recorded at the monitor well to evaluate anycross-well connections. Additional detail on how the data is analyzed isprovided below.

7. The cross-well interference information is then used to optimizedvarious well drilling, completion, fracturing, or production plans, andthose optimized plans are ultimately used to produce hydrocarbons fromthose or nearby wells.

The two wells can also be switched to further confirm the results, asstronger cross-well interference should show up in roughly the sameplaces along the wells.

The signal can be obtained in either vertical or horizontal wells, butthe main application is expected to be for use in horizontal wells inunconventional reservoirs.

The preferred optical fibers are those capable of working in harshenvironments. In harsh environments, like those found in oil and gasapplications, molecular hydrogen will diffuse from the environment,through virtually all materials, and nest in the core of the opticalfiber. This build-up of hydrogen causes attenuation to increase, ishighly variable, and affected by temperature, pressure, and hydrogenconcentration. Thus, fibers suitable for oil and gas applications arerequired. Exemplary fibers include the Ge-doped single mode andPure-core single mode from OFS Optics; Downhole Fibers from FIBERCORE;FiberPoint Sensors from Halliburton, and OmniWell from Weatherford.Schlumberger also offers a heterodyne distributed vibration sensingsystem for DAS and Ziebel offers Z-ROD, an optical fiber inside a carbonrod.

The Rayleigh and Ramen scattering that travels through the fibers iscollected by an interrogator connected to each fiber. It is alsopossible to use the same interrogator to monitor multiple fibers.Numerous interrogators are available to record optical signals includingsingle laser, dual laser, multiple laser, PINNACLE™ Gen-1 and Phase-1 orPhase-2, HALLIBURTON FIBERWATCH™, PROXFMION™ FBG-Interrogator, NATIONALINSTRUMENTS™ PXI, LUNA™, Silixa iDAS™, Optasense OPTASENSE®, FotechHelios® or other interrogator.

In one embodiment, Pinnacle Gen-1 and Phase-2 interrogators are used forthe recording the detected acoustic signals. In other embodiments,Silixa iDAS™ is used.

The interrogator collects the data in at least one data set. The rawdata may be at more than 6000 locations (frequently referred to as“channels”) with a variety of spatial separations from centimeters tometers along the fiber dependent upon length and required sensitivity.The gauge length may also be varied dependent upon fiber length and theinterrogator used, from 1-100 meters, including 1, 5, 10, 25, 50, 75 and100 meters. When recording, the measured optical phase is differentiatedin time, so the raw DAS data is linearly associated with strain ratealong the fiber. The low-pass filter does not affect these parameters.The gauge length and spatial spacing of the channels are determined bythe signal-to-noise level and manufacturer of the interrogator.

In some cases two or more interrogators may be used in parallel, one tocollect “noise”/high-frequency DAS and a second interrogator to collecttemperature rate of change/low-frequency DAS.

The workflow as described in WO2017156339 (expressly incorporated byreference in its entirety for all purposes) entails: i) spike noisereduction with 2D median filtering; ii) low frequency drift is removedwith either a joint inversion with DTS, or a time dependent drift from a“quiet” section of the DAS signal may be used to remove drift; iii)vibration noise is removed using velocity separability done with anFK-filter; iv) artifacts such as phase error impulse spikes may requirean envelope soft limit be used to threshold high amplitude noise; v) formeasurements outside the desired area temperature diffusion effect maybe removed by thermal recoupling; vi) thermal loss, mixing, andinteraction as one or more fluids travel through the wellbore may beapproximated by dynamic fluid correction.

In another embodiment, the workflow provides a series of signal to noiseratio (SNR) reduction techniques for production: i) Obtain a “raw”LF-DAS signal; ii) If spike noise is present, perform 2D Medianfiltering; iii) If LF drift is present: a. If temperature based, removewith DAS/DTS Joint Inversion; or b. If it is not temperature based or ifno DTS is available, remove with signal exclusion; iv) If fibervibration noise is present, Envelope soft limit and FK-Filter; v)Thermal recouple; and vi) Provide signal for later analysis.

Once converted, the transformed dataset can be displayed in any dataanalysis software capable of displaying DAS data. Examples in the oiland gas industry include FiberView, SeisSpace® or STIMWATCH® fromHalliburton, WellWatcher from Schlumberger. Other useful software thatis not specific to the oil and gas industry include Matlab, Spotfire,Python, and Excel. Most software can be used to convert the data into acontinuous record, transform the data, and down sample the data usingpre-programmed operations. However, operations to perform these stepscan be programmed if needed.

The displayed DAS signal or dataset can then be used as an interpretivetool to monitor well interference. In some scenarios, the data willinform the project manager interference with production allowing keydecisions in field development including well spacing, treatmentpressures, reservoir connectivity, and flow rates.

In some embodiments, the observed changes in the reservoir can beinputted into a reservoir modeling program to optimize the proposeddrilling trajectories, completions, hydraulic fracturing methods, andproduction plans for a given reservoir or reservoirs similar incharacter to the reservoir being monitored. The optimized programs canthen be implemented downhole and ultimately can then be used to produceoil or other hydrocarbon. The programs and methods described utilizenon-transitory machine-readable storage medium, which when executed byat least one processor of a computer, performs the steps of themethod(s) described herein.

Due to the nature of the data pre- and post-transform, parallelcomputing and data storage infrastructure created for data intensiveprojects, like seismic data processing, are used because they can easilyhandle the complete dataset. Hardware for implementing the inventivemethods may preferably include massively parallel and distributed Linuxclusters, which utilize both CPU and GPU architectures. Alternatively,the hardware may use a LINUX OS, XML universal interface run withsupercomputing facilities provided by Linux Networx, including thenext-generation Clusterworx Advanced cluster management system. Anothersystem is the Microsoft Windows 7 Enterprise or Ultimate Edition(64-bit, SP1) with Dual quad-core or hex-core processor, 64 GB RAMmemory with Fast rotational speed hard disk (10,000-15,000 rpm) or solidstate drive (300 GB) with NVIDIA Quadro K5000 graphics card and multiplehigh resolution monitors. Alternatively, many-cores can be used in thecomputing. A Linux based multi-core cluster has been used to process thedata in the examples described herein.

The disclosed methods include any one or more of the below embodimentsin any combination(s) thereof:

-   -   A method of evaluating cross-well interference, comprising:

a) providing a hydraulically fractured monitor well and a hydraulicallyfractured production well, said monitor well and said production well ina hydrocarbon formation;

b) providing one or more fiber optic cables along a length of saidmonitor well, wherein said one or more fiber optic cables are configuredfor low frequency distributed acoustic sensing (“DAS”) of <1 Hz and fordistributed temperature sensing (“DTS”);

c) shutting-in both wells until temperature and pressure equilibratesand then recording DAS data and DTS data for at least 2 hours in saidmonitor well;

d) opening said production well and producing hydrocarbon for a periodof time and continuing recording DAS data and DTS data throughout saidperiod of time;

e) analyzing said DAS data and said DTS data and determining whethersaid monitor well and said production well have interference based ontemperature fluctuations detected in said DAS data; and

f) identifying one or more locations where interference is occurringbased on locations where said temperature fluctuations are detected.

-   -   A method of optimizing hydrocarbon production from a reservoir,        comprising:

a) providing a hydraulically fractured monitor well and a hydraulicallyfractured production well in a reservoir, said monitor well and saidproduction well having potential interference;

b) providing one or more fiber optic cables along a length of saidmonitor well, wherein said one or more fiber optic cables are configuredfor low frequency distributed acoustic sensing (“DAS”) of <0.1 Hz andfor distributed temperature sensing (“DTS”);

c) shutting-in both wells for about 12 hours or more and then recordingDAS data and DTS data and pressure data for at least 2 hours in saidmonitor well;

d) variably opening said production well to vary pressure over a periodof time and continuing recording DAS data and DTS data and pressure datathroughout said period of time;

e) analyzing said DAS data and said DTS data and said pressure data;

f) determining whether said monitor well and said production well haveinterference based on temperature fluctuations detected in said DAS dataand fluctuations in said pressure data and determining a location alongsaid length where said interference is occurring based on temperaturefluctuations detected said DAS data; and

g) optimizing a hydrocarbon production plan based on said determinedinterference and said determined location.

-   -   A method as described herein, wherein DAS data is downsampled to        <1 Hz.    -   A method as described herein, wherein pressure is varied during        said opening step.    -   A method as described herein, further comprising measuring        pressure in said monitor well and said production well during        said opening step d or throughout said method.    -   A method as described herein, wherein said interference and said        location are confirmed by switching the identity of said monitor        well and said production well and repeating said method.    -   A method as described herein, wherein said one or more fiber        optic cables are cemented in behind a casing in said monitor        well.    -   A method as described herein, wherein said one or more fiber        optic cables are cemented in behind a casing in said monitor        well and said method further comprising correcting for a delay        in temperature change as it propagates through said case and        said cement to said one or more fiber optic cables.    -   A method as described herein, wherein said one or more fiber        optic cables are deployed into said monitor well via wireline,        coil tubing, or carbon rod.    -   A method as described herein, wherein said period of time is 1-5        hours.    -   A method as described herein, wherein said method further        includes estimating cross flow velocity of said interference by        comparing the DAS data with co-located temperature gauge or DTS        data.    -   A method as described herein, wherein said interference and said        location are confirmed by switching the identity of said monitor        well and said production well and repeating said method.    -   Any method described herein, including the further step of        printing, displaying or saving the initial, intermediate or        final (or both) datasets of the method to a non-transitory        computer readable memory.    -   Any method described herein, further including the step of using        the final datasets in a reservoir modeling program to predict        reservoir performance characteristics, such as fracturing,        production rates, total production levels, rock failures,        faults, wellbore failure, and the like.    -   Any method described herein, further including the step of using        said final datasets to design, implement, or optimize a        hydraulic fracturing program, a completion program or a        hydrocarbon production program in the same or in a similar        reservoir.    -   Any method described herein, further including the step of        producing hydrocarbon by said reservoir according to said        optimized programs.

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

As used herein, “providing” a well or a fiber optic cable does not implyor require contemporaneous drilling or installation of cables, as wellsmay already exist, and wells may already be fitted with the neededcables. Furthermore, it is recognized that the various steps of welldrilling, completion, logging and production may be undertaken bydifferent specialists and/or independent contractors, all under thedirection of the lease owner/operator, and these third party activitiesare to be considered as falling under activities by the owner/operator.

“Fracking”, as used herein, may refer to any human process used toinitiate and propagate a fracture in a rock formation, but excludesnatural processes that fracture formation, such as natural seismicevents. The fracture may be an existing fracture in the formation, ormay be initiated using a variety of techniques known in the art.“Hydraulic Fracking” means that pressure was applied via a fluid.

As used herein, “fracture parameters” refers to characteristics offractures made using hydraulic fracking and includes fracture growth,fracture height, fracture geometry, isolation conditions between stages,stress shadows and relaxation, fracture spacing, perforation clusterspacing, number of perforation clusters/stage, well spacing, job size,pumping pressure, heel pressure, proppant concentration, fluid andproppant distribution between perforation clusters, pumping volume,pumping rate and the like.

As used herein, a “fracture model” refers to a software program thatinputs well, rock and fracturing parameters and simulates fracturingresults in a model reservoir. Several such packages are available in theart, including SCHLUMBERGERS® PETREL® E&P, FRACCADE® or MANGROVE®software, STIMPLAN™, tNAVIGATOR™, SEEMYFRAC™, TERRAFRAC™, ENERFRAC®,PROP®, FRACPRO™, and the like. Add GOHFER® (Barree & Associates LLC) Forshale reservoirs, FRACMAN™ and MSHALE™ may be preferred. These modelscan be used with appropriate plugins or modifications needed to practicethe claimed methods.

By “fracture pattern”, we refer to the order in which the frack zonesare fractured.

The term “fracture complexity” refers to the degree of entanglement (orlack thereof) in the induced fractures. Fractures can range from simpleplanar fractures to complex planar fractures and network fracturebehavior. Further, the fracture complexity can change from near-well,mid-field, and far-field regions.

As used herein, the “Gaussian Kernel” or “radial basis function kernel”aka “RBF kernel” is a popular kernel function used in various kemelizedlearning algorithms. In particular, it is commonly used in supportvector machine classification.

As used herein, a “monitoring” well is a well nearby a producer that isused to monitor a producer. It produces samples and data for controlpurposes and could also be called a reference well or observation well.Obviously, well purposes can vary over time, and a production well canbe used to monitor another nearby production well, and thereafter beconverted back to production.

As used herein, “cross-well interference” is unintentional fluidcommunication between nearby wells, usually as a result of fracturesconnecting across the distance between the wells.

As used herein, “operation” well and “production” well are usedinterchangeably.

The term “many-core” as used herein denotes a computer architecturaldesign whose cores include CPUs and GPUs. Generally, the term “cores”has been applied to measure how many CPUs are on a giving computer chip.However, graphic cores are now being used to offset the work of CPUs.Essentially, many-core processors use both computer and graphicprocessing units as cores.

As used herein, the term “spike noise” refers to random bursts of noisein the acquired data.

As used herein, the term “semblance analysis” or “semblance function”refers to a process used in the refinement and study of seismic data togreatly increase the resolution of the data despite the presence ofbackground noise.

As used herein, the term “thermal signal moveouts” refers to thevelocity of the temperature signal.

As used herein, the term “FK filter” refers to a two-dimensional Fouriertransform over time and space where F is the frequency (Fouriertransform over time) and K refers to wave-number (Fourier transform overspace).

As used herein, the term “joint inversion” uses one data as a prioryconstraint in the inversion of other data. More sophisticated approachesinclude all data sets (in general two, three, or more) in a singleinverse algorithm.

As used herein, “drift removal” or “removing baseline drift” refers tocorrecting for a slow shifting of the baseline of the data. Thelow-frequency drift can be handled in two ways, depending on the type ofsignal being detected. If the desired low frequency DAS signal istemperature based and an independent temperature measurement is used,such as a distributed temperature sensor (DTS) which is commonlyacquired simultaneously with DAS, then a joint inversion can remove thedrift (as described in US20170260846). If the signal is not temperaturebased, or it is temperature based however there is not an accurateindependent measurement of absolute temperature, then we search for asection of channels of the fiber at a single time that is determined tohave a nonexistent, or low, signal strength. Once the quiet section isdetermined for each time sample, the median of the designated channelscan be used to extract a time dependent drift function which can then besubtracted from all channels at each time to remove the drift.

DRIFT t=MEDx(xqi t) xq is a set of quiet depth channels, t is time, MEDxis a median calculation of just the channel dimension, and DRIFT(t) isthe time dependent drift calculation for each time sample, t.

As used herein the term “median filter” refers to a nonlinear digitalfiltering technique, often used to remove noise. The main idea of themedian filter is to run through the signal entry by entry, replacingeach entry with the median of neighboring entries. The pattern ofneighbors is called the “window”, which slides, entry by entry, over theentire signal. For ID signals, the most obvious window is just the firstfew preceding and following entries, whereas for 2D (orhigher-dimensional) signals such as images, more complex window patternsare possible (such as “box” or “cross” patterns). Note that if thewindow has an odd number of entries, then the median is simple todefine: it is just the middle value after all the entries in the windoware sorted numerically. For an even number of entries, there is morethan one possible median, see median for more details.

As used herein, a “bandpass filter” refers to a device that passesfrequencies within a certain range and rejects (attenuates) frequenciesoutside that range.

As used herein, “envelope soft limiting techniques” refers to setting anamplitude range in the signal envelope, xx>x2>0, where xmax>x2, withxmax being the maximum envelope value in the investigation range. Anindividual envelope value e−{circumflex over ( )}s replaced ifxmax≥ex>x2 such that et=6l 2 (−L−x2)+x2.

xmax˜x2

As used herein, “thermal recoupling” refers to the removal of thetemperature diffusion effect caused by the sensor not being in directcontact with the measurement medium.

As used herein, “semblance techniques” refers to a calculated velocitymeasurement as used in US20170260839, incorporated by reference in itsentirety for all purposes. SNR increases calculated semblance accuracy.

As used herein, “diffusivity settings” refers to the thermal propertiesof the material between the measurement medium and the sensor.Additional details are provided in US20170342814 and US2018045040,incorporated by reference in its entirety for all purposes.

As used herein, “automatic gain control” refers to a technique used tokeep the signal amplitude over a particular window roughly constant sothat amplitude variations do not bias the analysis.

As used here, a “low frequency” signal refers to a frequency componentof the DAS signal that has a period of about 1 second or greater for aninterferometer length of a few meters. By using the phase of the lowfrequency components of the DAS signal, the temperature changes of thewell can be estimated and monitored in real time and with much higherprecision than is possible with a conventional short DTS measurement.The processor is configured to process DAS signal data to separate outthe low frequency oscillations present in DAS signals.

As described herein, the temperature difference required to shift thesignal by one interferometer fringe is about 0.15° C./L or up to afactor of two. For reasonable laser pulse widths this allows for thereal time monitoring of well temperature changes.

A “conversion” refers to mathematical transformation of data into aphysical measurement. As used herein, we convert DAS raw phase data intothe temperature variation.

An “inversion” refers to the estimation of the result by minimizing aparticular error function. As used herein, we invert the accuratetemperature measurement from DAS and DTS data.

The use of the word “a” or “an” when used in conjunction with the term“comprising” in the claims or the specification means one or more thanone, unless the context dictates otherwise.

The term “about” means the stated value plus or minus the margin oferror of measurement or plus or minus 10% if no method of measurement isindicated.

The use of the term “or” in the claims is used to mean “and/or” unlessexplicitly indicated to refer to alternatives only or if thealternatives are mutually exclusive.

The terms “comprise”, “have”, “include” and “contain” (and theirvariants) are open-ended linking verbs and allow the addition of otherelements when used in a claim.

The phrase “consisting of” is closed, and excludes all additionalelements.

The phrase “consisting essentially of” excludes additional materialelements, but allows the inclusions of non-material elements that do notsubstantially change the nature of the invention.

The following abbreviations are used herein:

ABBREVIATION TERM bbl oil barrel C-OTDR Coherent Optical Time DomainReflectometer CT Computer tomography DAS Distributed Acoustic SensingDTS Distributed temperature sensing G Gauges IU Interrogator Unit OTDROptical Time Domain Reflectometer P producer well S data well SNR signalto noise ratio SRV simulated rock volume TVD True vertical depth

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1: Conceptual model of cross flows in the monitor well induced bythe connection to the operation well.

FIG. 2: DTS temperature measurement in the monitor well during shut-inperiod. Left: temperature profile. Right: temperature spatial gradient.

FIG. 3: Borehole pressure gauge measurements. Top: Pressure measured inthe operation well, with associated well operations. Bottom: Pressuremeasured in the monitor well.

FIG. 4: Low-frequency DAS response in the monitor well compared withtemperature spatial gradient and borehole pressure during the operationwell choke changes. Colormap in the background is the DAS signal,vertical black curve is the temperature gradient profile measured by DTS(FIG. 2), and horizontal dashed line is the borehole pressure in themonitor well (FIG. 3).

FIG. 5: Comparison between the raw DAS data and the approximation usingEQ 4. Vertical solid line and horizontal dashed line in the middle panelshow the eigenvectors u₁(x) and v₁(t).

FIG. 6: Comparison between the spatial gradient of temperature dT/dxmeasured by DTS and the spatial eigenvector u₁(x) estimated from DASdata.

FIG. 7: Inversion results for the cross flow spatial variation. a)time-shifted u₁ and the model prediction αRdT/dx. b) inverted αR(x) andthe control points c. c) negative spatial gradient of R(x), withpositive value indicates outflow when the operation well opens.

FIG. 8: Same as FIG. 7, except the operations of the wells are switched.

FIG. 9: Spatial distribution of the outflow in FIG. 7c and FIG. 8 c.

FIG. 10: The comparison between the data from a DAS channel and theco-located borehole temperature gauge. The gauge data is differentiatedin time to obtain the temperature gradient.

FIG. 11: Calculated cross-flow velocity in the monitor well.

DETAILED DESCRIPTION

Herein, we use the data from two adjacent hydraulically fracturedhorizontal production wells. However, a similar procedure can be usedfor other kinds of wells.

Because DAS is a strain rate sensor and the fiber is mechanicallycoupled with the formation, strain from the minute temperaturevariations caused by interference can be detected. The DAS data arerecorded at an offset monitor well during production of an adjacentwell. The fiber-optic cables are preferably installed outside the casingand cemented in place. The raw data are sampled at 10 kHz continuouslyat more than 6000 locations along the wellbore, with 1 m spatialsampling and 5 m gauge length. The recorded optical phase isdifferentiated in time, hence the DAS data are linearly correlated withthe strain rate along the fiber.

The raw DAS data are down-sampled to 1 s after a low-pass anti-aliasingfilter (0-0.5 Hz) is applied. The data are then median filtered toremove any spiky noise. Another low-pass filter with a corner frequencyof 0.05 Hz is then applied. A DC drift with an amplitude around 0.1rad/s is removed from the data as well. The DC drift was channelinvariant and does not vary significantly with time. The drift noise ismost likely associated with interrogator noise. We estimated the DCdrift by calculating the median value of the channels that were out ofthe zone of interest at each time interval. Compared to the industrystandard waterfall visualizations, the low-frequency processing not onlyincreased the signal-to-noise ratio of the signal, but also preservedthe strain rate polarity, which is important for our interpretations.The strain change recorded by DAS at this frequency band can be causedby thermal variation and/or mechanic strain perturbation.

The DTS data are recorded at the same monitor well as the DAS data. TheDTS data can be recorded during or before the DAS data recording. Theraw data are sample at 5 minutes continuously with 1 ft spatialresolution. The data are averaged for several hours to obtain a reliableborehole temperature profile during shut-in. The recorded DTS data arecalibrated to remove the attenuation induced measurement error.

Because the wells are hydraulically fractured, the uneven completion ateach perforation induces a thermal spatial gradient during the shut-inperiod. FIG. 2 shows an example of the temperature profile in a monitorwell after a 24-hour shut-in, measured by DTS. The heel-most perforationin this well is located around 13000 ft, where the temperature dropsdramatically. Spatial temperature gradients around 10⁻³-10⁻² F/ft can beobserved in the stimulated section (13000-16500 ft), which is importantto create the signals required for this method.

After the operation well is opened, the borehole pressure drops due tothe production. This pressure perturbation propagates away from theoperation well through the conductive fracture network. If the monitorwell and the operation well are interconnected by fractures, thepressure in the monitor well will also be perturbed, whereas thepressure would otherwise not change. These pressure changes will causeflow from the monitor well towards the lower pressure zone near theproduction well, and that can be detected by temperature changes causesby the flow.

FIG. 3 shows the pressure response in both wells due to a series ofchoke-size changes in the operation well. In this example, the wellspacing was around 700 ft. Pressure was measured using bottom holesensors. The pressure perturbation near the monitor well was not uniformbecause the conductivity of the fractures was spatially heterogeneous.The highly connected fractures had lower pressure than the lessconnected fractures. The spatial gradient of pressure along the monitorwell induced cross flows in the monitor well borehole, with the fluidflowing from the weakly connected fractures towards the highly connectedfractures.

Due to the spatial gradient of temperature in the monitor well (FIG. 2),the cross flows produce small temperature perturbations, which can beapproximated as:

$\begin{matrix}{{\frac{dT}{dt} = {{- v}\frac{dT}{dx}}},} & (1)\end{matrix}$

where v is the cross-flow velocity, T is the monitor well boreholetemperature, t=time, and x is distance or position. We only consider theconvection induced temperature perturbation, while ignoring thetemperature mixing due to the reservoir fluid entering the boreholethrough perforations. We also ignore thermal conduction from surroundingformations. This assumption significantly simplifies the data analysis,and captures the majority of the signal amplitude.

Because the DAS signal at the ultra low-frequency band (<0.1 Hz) issensitive to temperature variations as small as 10⁻⁵° F., it can be usedto measure the cross-flow induced temperature perturbations. FIG. 4shows the DAS response at the monitor well during a series of chokechanges in the operation well, compared with the spatial gradient oftemperature measured by DTS (FIG. 2) and borehole pressure measured by apressure gauge (FIG. 3). The DAS response is highly correlated with thepressure changes in the temporal domain, and with spatial gradient oftemperature in the spatial domain. The DAS response is interpreted assmall thermal perturbations due to the cross flows between the monitorwell perforations. The cross flows are caused by the spatialheterogeneity of connectivity between the operation well and the monitorwell.

The thermal perturbation measured by DAS is mainly controlled by EQ 1,which can be rewritten as:

$\begin{matrix}{{{D\left( {x,t} \right)} = {{- \lambda}\;{v\left( {x,t} \right)}\frac{dT}{dx}(x)}},} & (2)\end{matrix}$where D is the low-frequency DAS signal, and λ is a constant thatconverts optical phase measured by DAS into temporal gradient oftemperature. If we assume the connectivity does not change during theperiod of data acquisition, we can further simplify the signal as:

$\begin{matrix}{{{D\left( {x,t} \right)} = {{{- \lambda}\;{V(t)}{R(x)}\frac{dT}{dx}(x)} = {{A(t)}{B(x)}}}},} & (3)\end{matrix}$where V(t) and R(x) describe how the magnitude of the cross-flowvelocity changes with time and space, respectively. From this equation,we can see that the DAS signal can be approximated by the product of twoone-dimensional, separable functions (A and B) that describe thevariation in time and space respectively.

The A(t) and B(x) can be obtained by applying singular-valuedecomposition (SVD) on the DAS data. The SVD operation decompose the DASdata D(x; t) into the summation of a series production of eigenvectorsand eigenvalues:

$\begin{matrix}{{{D\left( {x,t} \right)} = {{\sum\limits_{i}{{u_{i}(x)}\sigma_{i}{v_{i}(t)}}} \approx {{u_{1}(x)}\sigma_{1}{v_{1}(t)}}}},} & (4)\end{matrix}$where u_(i) and v_(i) are the left and right eigenvectors, and σ_(i) isthe eigenvalue. The eigenvalues are sorted in descending order. It isworth mentioning that u_(i) is a column vector while v_(i) is a rowvector, and the outer product of the two is a 2D matrix. Based on EQ 3,we can use the first (largest) eigenvalue and its correspondingeigenvectors to approximate the signal.

Extra processing steps may be considered to acquire better u₁ and v₁estimation. For example, u₁ and v₁ can be calculated independently usingdifferent section of the data. For the DAS data in FIG. 4, u₁(x) isevaluated using only the data from 1.5-4.5 hours, where the signal isstrongest and crossflow has subsided.

On the other hand, only the data from measured depth (MD) 13500 ft andbeyond is used to evaluate v₁(t) in order to avoid the effect of thelarge un-related signal around 13200 ft. u₁ and v₁ are then low-passfiltered to reduce the noise.

A comparison between the original DAS data and the approximation usingthe first (largest) eigenvalue σ1 and corresponding eigenvectors u₁ andv₁ (EQ 4) is shown in FIG. 5. This operation preserves the majority ofthe signal amplitude, while dramatically reduces the noise.

More importantly, it decomposes the DAS signal into two separate 1-Dfunctions that describe the temporal and spatial variations separately.In this method we assume the communication does not change within themeasurement period, which is usually only a few hours.

Substituting EQ 4 into 3 results in:

$\begin{matrix}{{{\frac{1}{\alpha}{u_{1}(x)}} = {{- {R(x)}}\frac{dT}{dx}(x)}}{{{\alpha\;\sigma_{1}{v_{1}(t)}} = {\lambda\;{V(t)}}},}} & (5)\end{matrix}$where a is a scaling constant.

FIG. 6 shows the comparison between the spatial gradient of temperaturedt/dx measured by DTS and the eigenvector u₁(x) estimated from DAS data.It is clear that parts of these two curves are correlated, while theother parts are anti-correlated. This is due to the different sign ofαR(x), which indicates the direction of the cross flows changes alongthe wellbore, like the one shown in FIG. 1. It is also noticeable thatthere is a small shift between these two curves, especially around14000-15000 ft. This spatial shift is due to the small moveout in thesignal due to the convection, which can be easily removed by dynamicwarping or other time-shift corrections.

αR(x) can be inverted by minimizing the misfit between u₁(x) and αR(x)dt/dx, which can be achieved by a least-square inversion minimizing thepenalty function:

$\begin{matrix}{\epsilon^{2} = {\int{\left( {{u_{1}(x)} + {\alpha\;{R(x)}\frac{dT}{dx}}} \right)^{2}.}}} & (6)\end{matrix}$

This inversion can be further stabilized by reducing degrees of freedomfor αR(x). Herein we use piecewise cubic interpolation with ten evenlyspaced control points, which can be performed by matrix operations:c=(G ^(T) G)⁻¹ G ^(T) u ₁,  (7)where coefficient matrix G=T_(x)M. T_(x) is a diagonal matrix with thediagonal elements equal todT/dx, and M is the interpolation matrix. c is the value at the controlpoints.

FIG. 7 shows the results of the least-square inversion. Positive valueof R(x) indicates toe-ward cross flows. The spatial gradient of R(x)indicates inflow/outflow at each section. As demonstrated in FIG. 1, thewell sections with stronger connections are associated with outflows(fluid flows from wellbore into formation) in the monitor well when theoperation well opens. In this case, there are three zones that indicatestronger connections, by ignoring the outflow at the heel (12500 ft)which is probably due to the edge effect of the cubical interpolation.

The connection between the wells should be bidirectional, which meansthat similar outflow locations should be observed if the operations ofthe wells are switched. FIG. 8 shows the result of the same inversion,except monitor and operation wells are interchanged. Three similaroutflow zones can be clearly observed in FIG. 8c , although the DASresponse in FIG. 8a is very different from that in FIG. 7 a.

FIG. 9 shows the spatial distribution of these outflow zones in bothwells by plotting them along the well paths. These outflow zonesindicate the locations of stronger connections between the two wells,which is consistent with the regional maximum stress direction, as wellas the cross-well fracture hits detected during completion, using themethod described in (Jin & Roy, 2017).

The connectivity between the wells can be further quantified byacquiring cross-flow velocity. This estimation requires knowing thescaling factor λ between the DAS optical phase measurement and thetemporal gradient of temperature. λ can be estimated by comparing theDAS response with the co-located temperature gauge or DTS data. FIG. 10shows the data comparison between a DAS channel in the monitor well andthe co-located borehole temperature gauge. The two signals are linearlycorrelated, and λ can be easily estimated by a linear regression. In thecase of evaluating λ using DTS, the workflow described in Jin et al.(2017b) can be referred.

After λ is known, 1/αV(t) can be easily obtained by:

$\begin{matrix}{{{\frac{1}{\alpha}{V(t)}} = \frac{\sigma_{1}u_{1}}{\lambda}},} & (8)\end{matrix}$which can then by multiplied by previously calculated R(x) to get thecross-flow velocity v(x; t) R(x)V (t). FIG. 11 shows the calculatedcross-flow velocity. Velocities as slow as 1 ft/hour can be detectedusing this method. If the radius of the borehole casing is known, thevolume rate of the outflow can also be calculated.

The analysis described herein provides a means to measure the spatialvariation of inter-well connectivity during the production stage. Thedemonstrated example is from two nearby hydraulically fracturedproduction wells. However, the method can be applied to any wells thathave a spatial gradient of temperature during a shut-in period.

It is worth emphasizing that the outflow zone locations shown in FIG. 9are not the only connected locations between the wells, but thelocations with stronger connectivity. Therefore, this method measureswell connectivity in relative terms. By combining the borehole pressuremeasurement (FIG. 3), it is possible to constrain the fractureconductivity using reservoir models. This method provides the spatialinformation of well interference that no other method provides, which isvaluable for completion and well spacing optimization.

This method can be applied on either temporarily deployed or permanentlyinstalled fiber cables. However, if the fiber used for DAS measurementis installed behind production casing and cemented in place, the heatconduction effect should be corrected when calculating the V(t), sincethe temporal variation of temperature can be delayed and attenuated asit propagates from the borehole, through the casing and cement, and intothe fiber. The correction can be applied by solving a 1D radialdiffusion equation, and is described in Kreuger (2017). A similarcorrection should be applied if the fiber is temporarily deployedthrough coil tubing or wirelines with large radius, where the thermalconductivity effect is not negligible.

The following references are each incorporated by reference in itsentirety for all purposes:

-   Awada, A., et al. (2016). Is that interference? A work flow for    identifying and analyzing communication through hydraulic fractures    in a multiwell pad. SPE Journal, 21 (05), 1-554.-   Jin, G., & Roy, B. (2017). Hydraulic-fracture geometry    characterization using low-frequency DAS signal. The Leading Edge,    36 (12), 975-980.-   Le Calvez, J. H., et al. (2007). Real-time microseismic monitoring    of hydraulic fracture treatment: a tool to improve completion and    reservoir management. AAPG Search and Discovery Article #90171    CSPG/CSEG/CWLS GeoConvention 2009, Calgary, Alberta, Canada, May    4-8, 2009-   SPE-140561-MS (2011) Molenaar M., et al., First Downhole Application    of Distributed Acoustic Sensing (DAS) for Hydraulic Fracturing    Monitoring and Diagnostics.-   SPE-149602 (2012) Johannessen K., et al., Distributed Acoustic    Sensing-a new way of listening to your well/reservoir-   SPE-173640-MS—Grayson, S., et al. (2015). Monitoring acid    stimulation treatments in naturally fractured reservoirs with    slickline distributed temperature sensing.-   SPE-179149-MS—Wheaton, B., et al. (2016). A case study of completion    effectiveness in the eagle ford shale using DAS/DTS observations and    hydraulic fracture modeling.-   SPE-186091-PA—Wu, K., et al. (2017). Mechanism analysis of well    interference in unconventional reservoirs: Insights from    fracture-geometry simulation between two horizontal wells.-   SPE-90541-MS—Ouyang, L.-B., et al. (2006). Flow profiling via    distributed temperature sensor (DTS) system-expectation and reality.-   URTEC-1581750-MS—Portis, D. H., et al. (2013). Searching for the    optimal well spacing in the eagle ford shale: A practical tool-kit.-   US20150146759 Temperature sensing using distributed acoustic    sensing.-   US20170260839 Hydraulic fracture monitoring by low-frequency DAS-   US20170260842 Low frequency distributed acoustic sensing-   US20170260846 Measuring downhole temperature by combining DAS/DTS    data-   US20170260849 DAS method of estimating fluid distribution-   US20170260854 Hydraulic fracture monitoring by low-frequency DAS.-   US20170342814 LOW-FREQUENCY DAS SNR IMPROVEMENT-   US2018045040 Production Logs From Distributed Acoustic Sensors    (42437).-   U.S. Pat. No. 8,505,625 Controlling well operations based on    monitored parameters of cement health.

The invention claimed is:
 1. A method of evaluating cross-well interference, comprising: a) providing a hydraulically fractured monitor well and a hydraulically fractured production well, said monitor well and said production well in a hydrocarbon formation; b) providing one or more fiber optic cables along a length of said monitor well, wherein said one or more fiber optic cables are configured for low frequency distributed acoustic sensing (“DAS”) of <1 Hz and for distributed temperature sensing (“DTS”); c) shutting-in both wells until temperature and pressure equilibrates and then recording DAS data and DTS data for at least 2 hours in said monitor well; d) opening said production well and producing hydrocarbon for a period of time and continuing recording DAS data and DTS data throughout said period of time; e) analyzing said DAS data and said DTS data and determining whether said monitor well has interference with said production well based on temperature fluctuations detected in said DAS data; and f) identifying one or more locations where interference is occurring based on locations where said temperature fluctuations are detected.
 2. The method of claim 1, wherein DAS data is downsampled to <1 Hz.
 3. The method of claim 1, wherein pressure is varied during said opening step.
 4. The method of claim 1, further comprising measuring pressure in said monitor well and said production well during said opening step d or throughout said method.
 5. The method of claim 1, wherein said interference and said location are confirmed by switching the identity of said monitor well and said production well and repeating said method.
 6. The method of claim 1, wherein said one or more fiber optic cables are cemented in behind a casing in said monitor well.
 7. The method of claim 1, wherein said one or more fiber optic cables are cemented in behind a casing in said monitor well and said method further comprising correcting for a delay in temperature change as it propagates through said case and said cement to said one or more fiber optic cables.
 8. The method of claim 1, wherein said one or more fiber optic cables are deployed into said monitor well via wireline, coil tubing, or carbon rod.
 9. The method of claim 1, wherein said period of time is 1-5 hours.
 10. The method of claim 1, wherein said method further includes estimating cross flow velocity of said interference by comparing the DAS data with co-located temperature gauge or DTS data.
 11. A method of optimizing hydrocarbon production from a reservoir, comprising: a) providing a hydraulically fractured monitor well and a hydraulically fractured production well in a reservoir, said monitor well and said production well having potential interference; b) providing one or more fiber optic cables along a length of said monitor well, wherein said one or more fiber optic cables are configured for low frequency distributed acoustic sensing (“DAS”) of <0.1 Hz and for distributed temperature sensing (“DTS”), and providing a borehole pressure gauge and one or more bottom hole pressure sensors in said monitor well for measuring pressure; c) shutting-in both wells for about 12 hours or more and then recording DAS data and DTS data and pressure data for at least 2 hours in said monitor well; d) variably opening said production well to vary pressure over a period of time and continuing recording DAS data and DTS data and pressure data throughout said period of time; e) analyzing said DAS data and said DTS data and said pressure data; f) determining whether said monitor well has interference with said production well based on temperature fluctuations detected in said DAS data and fluctuations in said pressure data and determining a location along said length where said interference is occurring based on temperature fluctuations detected in said DAS data; and g) optimizing a hydrocarbon production plan based on said determined interference and said determined location.
 12. The method of claim 11, wherein said interference and said location are confirmed by switching the identity of said monitor well and said production well and repeating said method.
 13. The method of claim 11, wherein said one or more fiber optic cables are cemented in behind a casing in said monitor well and further comprising correcting for a delay in temperature change as it propagates through said case and said cement to said one or more fiber optic cables.
 14. The method of claim 12, wherein said one or more fiber optic cables are deployed into said monitor well via wireline, coil tubing, or carbon rod.
 15. The method of claim 11, wherein said period of time is 1-5 hours.
 16. The method of claim 11, wherein said method further includes estimating cross flow velocity of said interference by comparing the DAS data with co-located temperature gauge or DTS data.
 17. The method of claim 11, wherein said method further includes estimating cross flow velocity of said interference by comparing the DAS data with co-located temperature gauge or DTS data.
 18. The method of claim 12, wherein said method further includes estimating cross flow velocity of said interference by comparing the DAS data with co-located temperature gauge or DTS data.
 19. The method of claim 13, wherein said method further includes estimating cross flow velocity of said interference by comparing the DAS data with co-located temperature gauge or DTS data. 